Search results for "Data Structures using C"
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R 916.00
Computing in Cause-Effect Structures
This book focuses on numerous examples of tasks represented by c-e structure. Cause–effect (c-e) structures are dynamic objects devised for algebraic and graphic description of realistic tasks. They constitute a formal system providing means to specify or implement (depending on degree of description generality) the tasks. They can be transformed, thus come under simplification, in accordance with rules-axioms of their algebra. Also, their properties can be inferred from the axioms. One objective of this book is presentation, by many realistic examples, of computing capability of c-e structures, without entering into mathematical details of their algebra. In particular, how computing with natural numbers and in propositional calculus can be performed by c-e structures and how to specify behavior of data structures. But also demonstration of many other tasks taken from the area of parallel processing, specified as c-e structures. Another objective is modelling or simulation by means of c-e structures, of other descriptive systems, devised for tasks from various fields. Also without formalizing by usage of functions between the systems. This concerns formalisms such as reaction systems, rough sets, Petri nets and CSP-like languages. Also on such, where temporal interdependence between actions matters. The presentation of examples is prevalently graphic, in the form of peculiar nets, but accompanied by their algebraic and set-theoretic expressions. A fairly complete exposition of concepts and properties of the algebra of cause-effect structures is in the previous book appeared in the Lecture Notes in Networks and Systems series. But basic notions of c-e structures are here provided for understanding the examples.
R 4,446.82
Computing in Cause-Effect Structures
This book focuses on numerous examples of tasks represented by c-e structure. Cause–effect (c-e) structures are dynamic objects devised for algebraic and graphic description of realistic tasks. They constitute a formal system providing means to specify or implement (depending on degree of description generality) the tasks. They can be transformed, thus come under simplification, in accordance with rules-axioms of their algebra. Also, their properties can be inferred from the axioms. One objective of this book is presentation, by many realistic examples, of computing capability of c-e structures, without entering into mathematical details of their algebra. In particular, how computing with natural numbers and in propositional calculus can be performed by c-e structures and how to specify behavior of data structures. But also demonstration of many other tasks taken from the area of parallel processing, specified as c-e structures. Another objective is modelling or simulation by means of c-e structures, of other descriptive systems, devised for tasks from various fields. Also without formalizing by usage of functions between the systems. This concerns formalisms such as reaction systems, rough sets, Petri nets and CSP-like languages. Also on such, where temporal interdependence between actions matters. The presentation of examples is prevalently graphic, in the form of peculiar nets, but accompanied by their algebraic and set-theoretic expressions. A fairly complete exposition of concepts and properties of the algebra of cause-effect structures is in the previous book appeared in the Lecture Notes in Networks and Systems series. But basic notions of c-e structures are here provided for understanding the examples.
R 4,230.08
Tree-Structure based Hybrid Computational Intelligence
Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. In this book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks. Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved.This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining will find the comprehensive coverage of this book invaluable.
R 4,030.00
Big Data in Complex Systems
This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.
R 4,030.00
Big Data in Complex Systems
This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.
R 4,030.00
Practical Applications Of Data Mining
Practical Applications of Data Mining emphasizes both theory and applications of data mining algorithms. Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples. This book will help any database and IT professional understand how to apply data mining techniques to real-world problems.Following an introduction to data mining principles, Practical Applications of Data Mining introduces association rules to describe the generation of rules as the first step in data mining. It covers classification and clustering methods to show how data can be classified to retrieve information from data. Statistical functions and drough set theory are discussed to demonstrate how statistical and rough set formulas can be used for data analytics and knowlege discovery. Neural networks is an important branch in computational intelligence. It is introduced and explored in the text to investigate the role of neural network algorithms in data analytics.
R 3,530.00
Tree-Structure based Hybrid Computational Intelligence
Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. In this book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks. Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved.This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining will find the comprehensive coverage of this book invaluable.
R 3,024.83
Hands-On Healthcare Data
Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, you'll be able to solve healthcare's most pressing problems: reducing cost of care, ensuring patients get the best treatment, and increasing accessibility for the underserved - once you learn how to access and make sense of all that data.This book provides pragmatic and hands-on solutions for working with healthcare data, from data extraction to cleaning and normalizing to feature engineering. Author Andrew Nguyen covers specific ML and deep learning examples with a focus on producing high-quality data. You'll discover how graph technologies help you connect disparate data sources so you can solve healthcare's most challenging problems using advanced analytics.With this book, you'll learn:The different types of healthcare data: electronic health records, clinical registries and trials, digital health tools, and claims dataThe challenges of working with healthcare data, especially when trying to aggregate data from multiple sourcesCurrent options for extracting structured data from clinical textHow to make trade-offs when using tools and frameworks for normalizing structured healthcare dataHow to harmonize healthcare data using terminologies, ontologies, and mappings and crosswalks
R 2,052.00
Learning Structure and Schemas from Documents
The rapidly growing volume of available digital documents of various formats and the possibility to access these through Internet-based technologies, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Due to the extremely large volumes of documents and to their unstructured form, most of the research efforts in this direction are dedicated to automatically infer structure and schemas that can help to better organize huge collections of documents and data. This book covers the latest advances in structure inference in heterogeneous collections of documents and data. The book brings a comprehensive view of the state-of-the-art in the area, presents some lessons learned and identifies new research issues, challenges and opportunities for further research agenda and developments. The selected chapters cover a broad range of research issues, from theoretical approaches to case studies and best practices in the field. Researcher, software developers, practitioners and students interested in the field of learning structure and schemas from documents will find the comprehensive coverage of this book useful for their research, academic, development and practice activity.
R 6,026.25
Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining
Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.
R 4,007.64
ML and Generative AI in the Data Lakehouse
In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using DatabricksLeverage LLMs to extract deeper, actionable insights from your business data residing in lakehousesDiscover how to integrate traditional ML and GenAI models for customized, scalable solutionsUtilize open source models to control costs while maintaining model performance and efficiencyImplement best practices for optimizing ML and GenAI models within the Databricks platform
R 1,978.00
Emerging Trends in the Evolution of Service-Oriented and Enterprise Architectures
This book presents emerging trends in the evolution of service-oriented and enterprise architectures. New architectures and methods of both business and IT are integrating services to support mobility systems, Internet of Things, Ubiquitous Computing, collaborative and adaptive business processes, Big Data, and Cloud ecosystems. They inspire current and future digital strategies and create new opportunities for the digital transformation of next digital products and services. Services Oriented Architectures (SOA) and Enterprise Architectures (EA) have emerged as a useful framework for developing interoperable, large-scale systems, typically implementing various standards, like Web Services, REST, and Microservices. Managing the adaptation and evolution of such systems presents a great challenge. Service-Oriented Architecture enables flexibility through loose coupling, both between the services themselves and between the IT organizations that manage them. Enterprises evolve continuously by transforming and extending their services, processes and information systems. Enterprise Architectures provide a holistic blueprint to help define the structure and operation of an organization with the goal of determining how an organization can most effectively achieve its objectives. The book proposes several approaches to address the challenges of the service-oriented evolution of digital enterprise and software architectures.
R 4,030.00
Emerging Trends in the Evolution of Service-Oriented and Enterprise Architectures
This book presents emerging trends in the evolution of service-oriented and enterprise architectures. New architectures and methods of both business and IT are integrating services to support mobility systems, Internet of Things, Ubiquitous Computing, collaborative and adaptive business processes, Big Data, and Cloud ecosystems. They inspire current and future digital strategies and create new opportunities for the digital transformation of next digital products and services. Services Oriented Architectures (SOA) and Enterprise Architectures (EA) have emerged as a useful framework for developing interoperable, large-scale systems, typically implementing various standards, like Web Services, REST, and Microservices. Managing the adaptation and evolution of such systems presents a great challenge. Service-Oriented Architecture enables flexibility through loose coupling, both between the services themselves and between the IT organizations that manage them. Enterprises evolve continuously by transforming and extending their services, processes and information systems. Enterprise Architectures provide a holistic blueprint to help define the structure and operation of an organization with the goal of determining how an organization can most effectively achieve its objectives. The book proposes several approaches to address the challenges of the service-oriented evolution of digital enterprise and software architectures.
R 4,030.00
Analytical Skills for AI and Data Science
While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs.Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You'll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.Break business decisions into stages that can be tackled using different skills from the analytical toolboxIdentify and embrace uncertainty in decision making and protect against common human biasesCustomize optimal decisions to different customers using predictive and prescriptive methods and technologiesAsk business questions that create high value through AI- and data-driven technologies
R 1,824.00
Data Science, Analytics and Machine Learning with R
Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.
R 4,414.00
Rigidly Framed Earth Retaining Structures
Structures placed on hillsides often present a number of challenges and a limited number of economical choices for site design. An option sometimes employed is to use the building frame as a retaining element, comprising a Rigidly Framed Earth Retaining Structure (RFERS). The relationship between temperature and earth pressure acting on RFERS, is explored in this monograph through a 4.5 year monitoring program of a heavily instrumented in service structure. The data indicated that the coefficient of earth pressure behind the monitored RFERS had a strong linear correlation with temperature. The study also revealed that thermal cycles, rather than lateral earth pressure, were the cause of failure in many structural elements.The book demonstrates that depending on the relative stiffness of the retained soil mass and that of the structural frame, the developed lateral earth pressure, during thermal expansion, can reach magnitudes several times larger than those determined using classical earth pressure theories. Additionally, a nearly perpetual lateral displacement away from the retained soil mass may occur at the free end of the RFERS leading to unacceptable serviceability problems. These results suggest that reinforced concrete structures designed for the flexural stresses imposed by the backfill soil will be inadequately reinforced to resist stresses produced during the expansion cycles.Parametric studies of single and multi-story RFERS with varying geometries and properties are also presented to investigate the effects of structural stiffness on the displacement of RFERS and the lateral earth pressure developed in the soil mass. These studies can aid the reader in selecting appropriate values of lateral earth pressure for the design of RFERS. Finally, simplified closed form equations that can be used to predict the lateral drift of RFERS are presented.KEY WORDS: Earth Pressure; Soil-Structure Interaction; Mechanics; Failure; Distress; Temperature; Thermal Effects; Concrete; Coefficient of Thermal Expansion; Segmental Bridges; Jointless Bridges; Integral Bridges; Geotechnical Instrumentation; Finite Element Modeling; FEM; Numerical Modeling.
R 5,658.94
